Method for correcting image brightness, image analysis device and analysis system

Abstract

The present invention provides a method for correcting image brightness, an image analysis device and an analysis system. The method for correcting image brightness is applied to an image analysis device, the image analysis device includes an imaging device, the imaging device includes a lens group and a camera, the lens group includes an objective lens, the imaging device is used to photograph an observed object in a sample, and the method includes: the image analysis device obtains a correction coefficient of image brightness; the image analysis device corrects the brightness of the image to be corrected according to the correction coefficient to obtain an image with uniform brightness. The method according to the present invention can obtain an image with uniform brightness, and improve the clarity and readability of the image output by the image analysis device.

Description

Technical Field

[0001] The present invention generally relates to the field of medical equipment technology, and more specifically to a method for correcting image brightness, an image analysis device and a sample analysis system. Background Art

[0002] The blood cell digital image analysis system (also called a film reader or cell image analysis device) is an instrument used to analyze cells on smears of peripheral blood, pathogens, bone marrow, body fluids, etc. Before reading the film, it is necessary to prepare a microscope slide with a sample coating (hereinafter referred to as a sample smear or slide). Its core module is a microscopic camera system, which captures the cells on the sample smear into color images and distinguishes the types of cells through intelligent recognition algorithms.

[0003] However, the current digital image analysis systems for blood cells in the industry generally have uneven illumination problems with their light sources. For images taken under a low-power microscope, the real information of the object cannot be seen clearly in areas that are too dark or too bright in the field of view, and they cannot be used for clinical diagnosis. When the sample is magnified and photographed under a high-power microscope, the field of view becomes smaller, and the image of the entire sample cannot be captured at once. Therefore, it is necessary to take multiple images of the sample and stitch them together. However, there are differences in illumination between different fields of view, which can easily lead to uneven illumination (i.e., uneven brightness) in the stitched image. Such uneven brightness images are clinically unacceptable.

[0004] In view of the existence of the above problems, the present application proposes a new method for correcting image brightness, an image analysis device and an analysis system to at least solve part of the above technical problems. Summary of the invention

[0005] The present invention is proposed to solve the above problems. Specifically, the present invention provides a method for correcting image brightness, which is applied to an image analysis device, wherein the image analysis device includes an imaging device, wherein the imaging device includes a lens group and a camera, wherein the lens group includes an objective lens, and the imaging device is used to photograph an observed object in a sample. The method includes:

[0006] The image analysis device obtains a correction coefficient of image brightness;

[0007] The image analyzing device corrects the brightness of the image to be corrected according to the correction coefficient to obtain an image with uniform brightness.

[0008] In one example, the image analysis device obtains a correction coefficient of image brightness, including:

[0009] The image analysis device obtains the preset correction coefficient;

[0010] Alternatively, the image analysis device obtains information about pixels of the computational image.

[0011] The correction coefficient is obtained based on the information of the pixel points of the calculated image.

[0012] In one example, the computational image is a single image taken by the imaging device; or

[0013] The computational image is a superimposed image formed by superimposing a plurality of images taken by the imaging device; or,

[0014] The computational image is a spliced ​​image formed by splicing a plurality of images taken by the imaging device.

[0015] In one example, obtaining the correction coefficient based on information of pixels of the computation image includes:

[0016] The image analyzing device obtains feature values ​​of at least some pixels of at least some areas of the computational image based on information of the pixels of the computational image;

[0017] Based on the target value and the characteristic value, a correction coefficient of pixel points in at least a partial area of ​​the operation image is determined.

[0018] In one example, the image analysis device obtains feature values ​​of at least some pixels of at least some areas in the computational image based on information of the pixels of the computational image, including:

[0019] The image analysis device obtains brightness distribution data of at least a part of the computational image through a fitting function based on information of pixel points of the computational image;

[0020] According to the brightness distribution data, a characteristic value of each pixel point in at least a partial area of ​​the operation image is obtained, and the characteristic value of each pixel point is: the corresponding fitting pixel value of each pixel point in the brightness distribution data.

[0021] In one example, the at least partial area includes all areas of the calculated image, and obtaining, according to the brightness distribution data, a feature value of each pixel point in at least a partial area of ​​the calculated image includes:

[0022] According to the brightness distribution data, a characteristic value of each pixel in the operation image is obtained.

[0023] In one example, determining the correction coefficient of the pixel points of the at least partial area based on the target value and the characteristic value includes:

[0024] A correction coefficient matrix is ​​determined according to the target value and the characteristic value of each pixel in the operation image.

[0025] In one example, the step of correcting the brightness of the image to be corrected according to the correction coefficient to obtain an image with uniform brightness includes:

[0026] The brightness of corresponding pixels in the image to be corrected is corrected according to the correction coefficient matrix to obtain a corrected image with uniform brightness.

[0027] In one example, the image analysis device obtains brightness distribution data of at least a partial area in the computational image by fitting a function based on information of pixels of the computational image, including:

[0028] Acquire location information of multiple sub-regions in the at least partial region and a statistical value of each of the sub-regions, wherein the statistical value of each of the sub-regions includes one of the following values: an average pixel value of the pixels included in each sub-region, a median value of the pixel values ​​of the pixels included in each sub-region, and a pixel value corresponding to a peak value of a pixel histogram of each sub-region;

[0029] Solving the parameters of the fitting function according to the position information and the statistical value of each sub-region to determine the brightness distribution function of at least a part of the region in the operation image;

[0030] The brightness distribution data of at least a partial area in the operation image is determined according to the brightness distribution function.

[0031] In one example, the fitting function is a distribution function of brightness in the calculated image in a two-dimensional plane; the fitting function includes one of the following functions: a binary quadratic polynomial, an exponential function, and a power function.

[0032] In one example, the image analysis device obtains feature values ​​of at least some pixels of at least some areas in the computational image based on information of the pixels of the computational image, including:

[0033] Dividing at least a portion of the computational image into a plurality of sub-regions;

[0034] Acquire a characteristic value of each of the sub-regions as a characteristic value of a pixel point of each sub-region, wherein the characteristic value of each of the sub-regions includes one of the following statistical values: an average pixel value of the pixel points included in each sub-region, a median value of the pixel values ​​of the pixel points included in each sub-region, and a pixel value corresponding to a peak value of a pixel histogram of each sub-region.

[0035] In one example, the image analysis device obtains feature values ​​of at least some pixels of at least some areas in the computational image based on information of the pixels of the computational image, including:

[0036] dividing the at least partial area into a plurality of sub-areas;

[0037] Based on the information of the pixel points of the calculated image, the statistical values ​​of some sub-regions among the multiple sub-regions are obtained, and the statistical values ​​of each sub-region are used as the characteristic values ​​of the pixel points in the sub-region, wherein the statistical values ​​of each of the sub-regions of the some sub-regions include one of the following values: the average pixel value of the pixel points included in each sub-region, the median value of the pixel values ​​of the pixel points included in each sub-region, and the pixel value corresponding to the peak value of the pixel histogram of each sub-region.

[0038] In one example, determining correction coefficients of pixels of at least a portion of the computation image based on the target value and the characteristic value includes:

[0039] Determine a correction coefficient for a pixel point in each of the subregions according to the target value and a characteristic value of a pixel point in a part of the subregions;

[0040] According to the correction coefficient corresponding to the pixel point in each sub-region of the partial sub-region, the correction coefficient corresponding to the pixel point in other sub-regions of the at least partial region is solved to obtain the correction coefficient of all the pixel points in the at least partial region.

[0041] In one example, the correction coefficient includes a first correction coefficient or a second correction coefficient or a third correction coefficient or a fourth correction coefficient, wherein,

[0042] The first correction coefficient is the ratio of the target value / characteristic value.

[0043] The second correction coefficient is the ratio of the characteristic value / target value,

[0044] The third correction coefficient is the difference between the target value and the characteristic value.

[0045] The fourth correction coefficient is a difference obtained by subtracting the target value from the characteristic value.

[0046] In one example, correcting the brightness of the image to be corrected according to the correction coefficient to obtain an image with uniform brightness includes:

[0047] The brightness of the image to be corrected is corrected according to the actual pixel value of each pixel point in at least a partial area of ​​the image to be corrected and the correction coefficient corresponding to each pixel point to obtain an image with uniform brightness.

[0048] In one example, correcting the brightness of the image to be corrected according to the actual pixel value of each pixel point in at least a portion of the image to be corrected and the correction coefficient corresponding to each pixel point to obtain an image with uniform brightness includes:

[0049] multiplying the actual pixel value of each pixel point in at least a portion of the image to be corrected by the first correction coefficient corresponding to each pixel point to obtain the corrected pixel value of each pixel point, or dividing the actual pixel value of each pixel point in at least a portion of the image to be corrected by the second correction coefficient corresponding to each pixel point to obtain the corrected pixel value of each pixel point;

[0050] The image to be corrected is updated according to the corrected pixel values ​​to obtain an image with uniform brightness.

[0051] In one example, correcting the brightness of the image to be corrected according to the actual pixel value of each pixel point in at least a portion of the image to be corrected and the correction coefficient corresponding to each pixel point to obtain an image with uniform brightness includes:

[0052] Adding the actual pixel value of each pixel in at least a partial area of ​​the image to be corrected to the third correction coefficient corresponding to each pixel to obtain the corrected pixel value of each pixel, or subtracting the actual pixel value of each pixel in at least a partial area of ​​the image to be corrected from the fourth correction coefficient corresponding to each pixel to obtain the corrected pixel value of each pixel;

[0053] The image to be corrected is updated according to the corrected pixel values ​​to obtain an image with uniform brightness.

[0054] In one example, the target value is pre-set in the image analysis device, or is determined based on information of pixels of the image to be corrected.

[0055] In one example, the target value is determined based on information of a pixel point of the image to be corrected, wherein the target value includes one of the following values:

[0056] The pixel value of any pixel in the image to be corrected;

[0057] The statistical value of the pixel values ​​of at least a part of the image to be corrected is a mean pixel value of the pixels included in at least the part of the image, a median value of the pixel values ​​of the pixels included in at least the part of the image, or a pixel value corresponding to a peak value of a pixel histogram of at least the part of the image;

[0058] The fitted pixel value corresponding to any pixel point of the image to be corrected in the brightness distribution data;

[0059] The statistical value of the fitted pixel value corresponding to at least a partial area of ​​the image to be corrected in the brightness distribution data is the average value of the fitted pixel values ​​of the pixel points included in at least a partial area, the median value of the fitted pixel values ​​of the pixel points included in at least a partial area, or the fitted pixel value corresponding to the peak value of the fitted pixel histogram of at least a partial area.

[0060] In one example, the target value is pre-set in the image analysis device, wherein the target value is a preset empirical value, or a preset value determined based on information of pixel points of an image pre-taken by the imaging device.

[0061] In one example, the method further includes:

[0062] The correction coefficient is updated at every preset time interval.

[0063] In another aspect, the present invention provides an image analysis device, comprising:

[0064] An imaging device, comprising a lens group and a camera, for photographing a sample, wherein the lens group comprises an objective lens;

[0065] A sample moving device, the sample moving device is used to move the sample relative to the imaging device so that the imaging device captures an image of a specific area in the sample;

[0066] The processor is used to execute the aforementioned method for correcting image brightness to obtain an image with uniform brightness.

[0067] In one example, the image analysis device further includes a display device, and the display device is used to display the corrected image.

[0068] Another aspect of the present invention provides a sample analysis system, the sample analysis system comprising:

[0069] A blood analyzer, used to perform routine blood tests on samples to be tested;

[0070] A smear preparation device, used to prepare the sample to be tested on a smear;

[0071] The aforementioned image analysis device is used to capture and analyze the image of the sample to be tested on the smear;

[0072] A control device is communicatively connected with the blood analyzer, the smear preparation device and the image analysis device.

[0073] By correcting the image brightness through the method of the present invention, the clarity and readability of the image output by the image analysis device can be improved, the appearance of a large number of unqualified images can be avoided, and the user's review efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0075] Figure 1 A schematic block diagram of an image analysis device in one embodiment of the present invention is shown;

[0076] Figure 2 A schematic diagram showing the structure of an image analysis device in one embodiment of the present invention is shown;

[0077] Figure 3 A flow chart showing a method for correcting image brightness in one embodiment of the present invention is shown;

[0078] Figure 4 A schematic diagram of dividing an image to be fitted into blocks in one embodiment of the present invention is shown, the left figure shows an image to be fitted, and the right figure shows the blocks of the image to be fitted;

[0079] Figure 5 A schematic diagram showing a brightness distribution surface of an image in one embodiment of the present invention is shown;

[0080] Figure 6 A schematic diagram of an image coordinate system when performing one-dimensional distribution fitting in one embodiment of the present invention is shown;

[0081] Figure 7 A schematic diagram showing a distribution curve obtained after performing one-dimensional distribution fitting in one embodiment of the present invention is shown;

[0082] Figure 8 A schematic diagram showing dividing an image into a plurality of image blocks in one embodiment of the present invention is shown;

[0083] Fig. 9 A schematic diagram showing a method of obtaining correction coefficients of all pixels in an image by interpolation in one embodiment of the present invention;

[0084] Fig.10 A schematic diagram of a sample analysis system in one embodiment of the present invention is shown;

[0085] Fig.11 A schematic diagram of a control device in one embodiment of the present invention is shown. DETAILED DESCRIPTION

[0086] In order to make the purpose, technical scheme and advantages of the present invention more obvious, the exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention, and it should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described in the present invention, all other embodiments obtained by those skilled in the art without creative work should fall within the protection scope of the present invention.

[0087] In the following description, a large number of specific details are given in order to provide a more thorough understanding of the present invention. However, it is obvious to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features known in the art are not described. It should be understood that the present invention can be implemented in different forms and should not be interpreted as being limited to the embodiments proposed here. On the contrary, providing these embodiments will make the disclosure thorough and complete, and the scope of the present invention will be fully conveyed to those skilled in the art.

[0088] The purpose of the terms used herein is only to describe specific embodiments and is not intended to be limiting of the present invention. When used herein, the singular forms "one", "an" and "said / the" are also intended to include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "consisting of" and / or "comprising", when used in this specification, determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0089] In order to fully understand the present invention, a detailed structure will be proposed in the following description to illustrate the technical solution proposed by the present invention. The optional embodiments of the present invention are described in detail as follows, but in addition to these detailed descriptions, the present invention may also have other implementations.

[0090] Specifically, the method for compensating lens switching errors, the image analysis device, and the sample analysis system of the present application are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the features of the following embodiments and implementations may be combined with each other.

[0091] First, refer to Figure 1 , an image analysis device in one embodiment of the present invention is described, wherein, Figure 1 A schematic block diagram of an image analysis device in one embodiment of the present invention is shown; Figure 2 A schematic structural diagram of an image analysis device in one embodiment of the present invention is shown.

[0092] As an example, Figure 1 As shown, the present invention provides an image analysis device 10, which includes an imaging device 11, a sample moving device 12, a processor 13 and a display device 14, wherein the imaging device 11 includes a lens group 112 and a camera 111, the lens group 112 includes at least a first lens 1121 and a second lens 1122, and the imaging device is used to photograph the observed object in the sample.

[0093] In one example, the image analysis device may be a cell image analysis device, which is used to capture and analyze images of objects to be observed in a sample, wherein the sample may include, for example, a blood sample, a body fluid sample, a urine sample, etc. The sample may be smeared on a smear by a smear preparation device, or the sample may be a tissue section, which may also be prepared and placed on a smear to facilitate user observation of the sample, or the sample may also be placed on other suitable carriers. The imaging device may be used to capture and analyze different objects to be observed in different samples, for example, cells (such as white blood cells, red blood cells, etc.) in a blood sample may be captured, or crystals in a urine sample, or cells in a tissue section may be captured, etc.

[0094] The sample moving device 12 (such as a stage) can be used to move the sample relative to the imaging device so that the imaging device 11 captures an image of a specific area of ​​the sample. For example, when the sample is smeared on a smear, the sample moving device 12 is used to place the smear and move the smear relative to the imaging device so that the imaging device captures an image of a specific area of ​​the sample.

[0095] like Figure 2 As shown, the lens group 112 includes an objective lens, for example, multiple objective lenses with different magnifications. In one example, the lens group 112 includes a first lens 1121 and a second lens 1122, wherein the first lens 1121 and the second lens 1122 are both objective lenses. The first lens 1121 and the second lens 1122 are objective lenses with different magnifications, for example, the first lens 1121 may be a 10x objective lens, and the second lens 1122 may be a 100x objective lens.

[0096] like Figure 2 As shown, the lens group may further include a third lens 1123, which may be, for example, a 40x objective lens. The lens group may further include an adapter tube 1120 and an eyepiece.

[0097] The magnifications of the above-mentioned first lens, second lens and third lens are only examples. In other examples, the first lens, second lens and third lens may also be objective lenses with other magnifications. For example, the first lens 1121 is a 100x objective lens, the second lens is a 10x objective lens, and the third lens is a 40x objective lens; or, the first lens 1121 is a 100x objective lens, the second lens is a 40x objective lens, and the third lens 1123 is a 10x objective lens; or, the first lens 1121 can be a 40x objective lens, the second lens is a 100x objective lens, and the third lens 1123 is a 10x objective lens, etc.

[0098] In one example, the image analysis device further includes an identification device (not shown), a glass slide clamping device (not shown) and a smear recovery device (not shown). The identification device (not shown) is used to identify the identity information of the smear, the glass slide clamping device is used to clamp the smear from the identification device to the sample moving device 12 for detection, and the smear recovery device is used to place the detected smear.

[0099] The image analysis device further includes a slide basket loading device (not shown) for loading a slide basket containing a smear to be tested, and a slide clamping device for clamping the slide to be tested in the slide basket loaded on the slide basket loading device to the identification device for identification information. The slide basket loading device is connected to a transmission track, which connects the smear preparation device and the image analysis device so that the smear prepared by the smear preparation device can be transported to the image analysis device.

[0100] In one example, the image analysis device further includes an input device (not shown), which may be a device used by a user to input instructions, and may include one or more of a keyboard, a trackball, a mouse, a microphone, a touch screen, and the like.

[0101] In this embodiment, Figure 1 As shown, the image analysis device may further include a display device 14 for displaying information input by a user or information provided to a user and various image user interfaces of the image analysis device. These image user interfaces may be composed of images, texts, icons, videos and any combination thereof. In the present embodiment, the display device may display various visual data output by the processor, such as an image before correction, an image after correction, a brightness distribution surface, etc. The display device may include a display panel. Optionally, the display panel may be configured in the form of a liquid crystal display device (LCD, Liquid Crystal Display), an organic light-emitting diode (OLED, Organic Light-Emitting Diode), etc.

[0102] The image analysis device according to the embodiment of the present invention further includes a communication interface (not shown) for communication between the various devices of the image analysis device and other devices outside the image analysis device or between the various components of the image analysis device, including wired or wireless communication. It should be understood that the image analysis device according to the embodiment of the present invention is not limited by the communication interface, and both the interface of the currently known communication protocol and the interface of the communication protocol to be developed in the future can be used in the image analysis device according to the embodiment of the present invention to realize the function of communicating with the outside world through the network, and no limitation is made here.

[0103] like Figure 1 As shown, the image analysis device of the embodiment of the present invention also includes a memory (not shown) and a processor 13, and the memory is used to store data and executable instructions, such as for storing system programs of the image analysis device, various application programs, or algorithms for implementing various specific functions. It can include one or more computer program products, and the computer program product can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory can include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory can include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. In addition, during the sample detection process, if there is data that needs to be stored locally, it can be stored in the memory.

[0104] The processor 13 may be a central processing unit (CPU), a graphics processing unit (GPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the image analysis device to perform desired functions. For example, the processor may include one or more embedded processors, processor cores, microprocessors, logic circuits, hardware finite state machines (FSMs), digital signal processors (DSPs), graphics processing units (GPUs), or combinations thereof. Thus, the processor 13 may execute some or all of the steps or any combination of the steps in the method for correcting image brightness in various embodiments of the present application. For a description of the method for correcting image brightness, refer to the description of the embodiments below.

[0105] When the image analysis device according to the embodiment of the present invention is running, a corresponding operation interface can be provided for the operator to operate. The above operation interface can include various corresponding controls, such as an identification box or a menu bar, so that the operator can input operation instructions on the operation interface according to actual usage conditions to realize the analysis of the observed objects, such as cells, on the sample smear through the image analysis device.

[0106] The light source of the image analysis device generally has the problem of uneven illumination. For images taken under a low-power microscope, the real information of the object cannot be seen clearly in areas that are too dark or too bright in the field of view, and it cannot be used for clinical diagnosis. When the sample is magnified and photographed under a high-power microscope, the shooting field of view becomes smaller, and the image of the entire sample cannot be captured at once. Therefore, it is necessary to take multiple images of the sample and stitch the multiple images together. However, there are differences in illumination between different fields of view, which can easily lead to uneven illumination (i.e., uneven brightness) in the stitched image. Such uneven brightness images are clinically unacceptable.

[0107] In view of the above problems, the present application proposes a method for correcting image brightness, which is applied to an image analysis device, wherein the image analysis device includes an imaging device, the imaging device includes a lens group and a camera, the lens group includes an objective lens, and the imaging device is used to photograph an observed object in a sample. The method includes: the image analysis device obtains a correction coefficient for image brightness; the image analysis device corrects the brightness of the image to be corrected according to the correction coefficient to obtain an image with uniform brightness. By correcting the image using the method of the present invention, the clarity and readability of the image output by the image analysis device can be improved, a large number of unqualified images can be avoided, and the user's review efficiency can be improved.

[0108] Below, reference Figures 3 to 9 The method for correcting image brightness of the present invention is described, wherein: Figure 3 A flow chart showing a method for correcting image brightness in one embodiment of the present invention is shown; Figure 4 A schematic diagram of dividing an image to be fitted into blocks in one embodiment of the present invention is shown, the left figure shows an image to be fitted, and the right figure shows the blocks of the image to be fitted; Figure 5 A schematic diagram showing a brightness distribution surface of an image in one embodiment of the present invention is shown; Figure 6 A schematic diagram of an image coordinate system when performing one-dimensional distribution fitting in one embodiment of the present invention is shown; Figure 7 A schematic diagram showing a distribution curve obtained after performing one-dimensional distribution fitting in one embodiment of the present invention is shown; Figure 8 A schematic diagram showing dividing an image into a plurality of image blocks in one embodiment of the present invention is shown; Fig. 9 A schematic diagram showing a method of obtaining correction coefficients of all pixels in an image by interpolation in one embodiment of the present invention;

[0109] As an example, Figure 3 As shown, the method for correcting image brightness according to the embodiment of the present invention includes the following steps S301 to S302, and the method can be implemented based on the aforementioned image analysis device.

[0110] First, in step S301, the image analysis device obtains a correction coefficient of image brightness.

[0111] In one example, the image analysis device obtains a correction coefficient for image brightness, including: the image analysis device obtains a preset correction coefficient, that is, the correction coefficient has been pre-stored in the image analysis device, and when needed, the image analysis device only needs to directly call the correction coefficient. This method is simple and can effectively correct the image brightness.

[0112] The correction coefficient can also be obtained based on the information of the image captured by the imaging device. For example, the image analysis device obtains the correction coefficient of the image brightness, including: the image analysis device obtains the information of the pixel points of the calculated image, and obtains the correction coefficient based on the information of the pixel points of the calculated image. Using the calculated image to determine the correction coefficient and then adjust the brightness of the image can make the brightness of the image more uniform while maintaining the image's own information.

[0113] In one example, the computational image is a single image taken by the imaging device, and the correction coefficient is obtained using information of pixels of the single image. For example, when the acquired image is an image taken under a low-power lens, it is sufficient to acquire only one image taken under the low-power lens, and the low-power lens refers to an objective lens with a magnification lower than a threshold magnification, such as an objective lens with a magnification of 10 times.

[0114] The information of the pixels of the image captured by the imaging device may refer to the current brightness information of the pixels of the image (eg, the pixel value of each pixel in the image).

[0115] It is worth mentioning that in this article, the calculation of the information of the pixel points of the image captured by the imaging device, such as the calculation of the pixel value of the pixel point in the image, can be performed in any space such as R / G / B / grayscale / brightness, that is, the pixel value of the pixel point in the image can be calculated by the characteristic parameters of the image, for example, the characteristic parameters include one of the following parameters: R / G / B, grayscale, brightness, or other parameters that can reflect the brightness information of the image. The pixel value of this application can reflect the brightness information of the image, and the pixel value can also be a brightness value.

[0116] In another example, the computational image is a superimposed image formed by superimposing multiple images taken by the imaging device; due to the distribution of cells in the image, there will be a slight deviation in the brightness reflected by the image pixel values ​​in the area. By superimposing multiple images, the pixel values ​​of the superimposed image can better reflect the actual brightness.

[0117] When the computation image is a superimposed image, the image analysis device obtains information of pixels of the computation image, including: superimposing information of corresponding pixels of multiple images taken by the imaging device (e.g., pixel values ​​of pixels) and respectively calculating average pixel values ​​to obtain information of each pixel of the computation image (e.g., pixel value of each pixel). Alternatively, the information of each pixel of the computation image may be obtained based on the information of corresponding pixels of multiple images taken by the imaging device by other suitable methods.

[0118] It is worth mentioning that the multiple images used for superimposing and obtaining the computational image have the same size as the computational image. The multiple images may be multiple images taken at the same position, or may be images taken at different positions.

[0119] In other examples, the computational image is a stitched image formed by stitching together multiple images taken by the imaging device. For example, when the acquired image is an image taken under a high-power microscope, multiple images taken under a high-power microscope (e.g., images of multiple different areas of a sample taken by an imaging device) can be stitched together to obtain a stitched image, and the high-power microscope refers to an objective lens with a magnification higher than a threshold magnification, such as an objective lens with a magnification of 40 times or 100 times. The stitched image is subsequently subjected to multiple steps such as fitting a brightness distribution surface, determining a correction coefficient, and performing correction according to the correction coefficient. The image stitching method includes, but is not limited to, method one: extracting feature points from single-view images adjacent to each other in physical position, and feature factors include, but are not limited to, SIFT, SURF, Harris corner points, ORB, etc., and then performing image feature matching, and finally stitching together to form a stitched image (e.g., a complete full-view image). Method two: Determine the size of the overlapping area of ​​two adjacent single-view images, and then perform weighted averaging of the overlapping part to obtain an image of the overlapping part, and finally stitch together to obtain a stitched image (e.g., a complete full-view image).

[0120] Regardless of which method is used, without processing the image, the light source of the image analysis device has an uneven illumination problem, which leads to uneven illumination of the image, that is, uneven brightness, making the image unusable for clinical diagnosis. Therefore, the method of the present invention corrects the brightness of these images.

[0121] In this article, the image taken by the imaging device is an image of the observed object in the sample taken by the imaging device. The sample may include, for example, a blood sample, a body fluid sample, a urine sample, etc., and the observed object in the sample may be, for example, cells in the blood sample (including but not limited to white blood cells, red blood cells, etc.), crystals in the urine sample, cells in the tissue section, etc. The sample can be smeared on the smear by a smear preparation device, the sample can also be a tissue section, and the tissue section can also be prepared and placed on the smear to facilitate the user to observe the sample, or the sample can also be placed on other suitable carriers.

[0122] The image captured by the imaging device may be an image captured under an objective lens of any magnification. Alternatively, after the image analysis device captures an image of the observed object, the image analysis device uses the image for calculation of the correction coefficient, and then uses the obtained correction coefficient to correct the brightness of the image, or the correction coefficient may be used to correct the brightness of multiple images, or the image analysis device may directly retrieve any image in the storage space of the image analysis device for subsequent determination of the correction coefficient, etc.

[0123] The correction coefficient can be obtained based on the pixel information of the operation image in the following manner. In one example, the correction coefficient is obtained based on the pixel information of the operation image, including: the image analysis device obtains the characteristic value of at least part of the pixel points in at least part of the area of ​​the operation image based on the pixel information of the operation image; based on the target value and the characteristic value, the correction coefficient of the pixel points in at least part of the area of ​​the operation image is determined. In this article, the characteristic value of the pixel point is also the brightness characteristic value of the pixel point, which is used to reflect the brightness information of the pixel point.

[0124] The correction coefficient may be obtained based on the information of the pixel points of the computation image. The following describes the methods for determining the correction coefficient of the pixel points of at least a partial area in the computation image based on different methods in turn.

[0125] In one embodiment, the correction coefficient can be determined by fitting an image through a fitting function, and the image analysis device obtains characteristic values ​​of at least part of the pixel points in at least part of the area of ​​the computational image based on the information of the pixel points of the computational image, including: the image analysis device obtains brightness distribution data of at least part of the area of ​​the computational image through a fitting function based on the information of the pixel points of the computational image; optionally, the brightness distribution data can be a two-dimensional brightness distribution surface, or can also be a one-dimensional brightness distribution curve, and the characteristic value of each pixel point in at least part of the area of ​​the computational image is obtained according to the brightness distribution data, and the characteristic value of each pixel point is: the corresponding fitting pixel value of each pixel point in the brightness distribution data.

[0126] Wherein, the image analysis device obtains the brightness distribution data of at least a part of the area in the computational image through a fitting function based on the information of the pixel points of the computational image. For example, if at least a part of the area includes all the areas of the computational image, the brightness distribution data of all the areas of the image can be obtained through a fitting function based on the information of the pixel points of the computational image. Then, according to the brightness distribution data, the fitting pixel value of each pixel point in at least a part of the area in the computational image is obtained, and the characteristic value of each pixel point is: the corresponding fitting pixel value of each pixel point in the brightness distribution data. For example, if the brightness distribution data is the brightness distribution data of all the areas of the image, then according to the brightness distribution data, the fitting pixel value of each pixel point in the computational image (that is, the characteristic value of each pixel point) can be obtained.

[0127] In this article, at least a partial area in the calculated image can be all areas in the image (that is, the entire image) or can also be a partial area in the calculated image, where the partial area can be an area in the calculated image where the pixel value is lower than the target value, or an area higher than the target value, or the partial area is an area where the difference between the pixel value of the pixel point and the target value is greater than a threshold difference, or any area in the calculated image.

[0128] If the fitting function is the distribution function of the light in the computational image on a two-dimensional plane, the brightness distribution data can be presented in the form of a brightness distribution surface, such as Figure 5 As shown, if the fitting function is the distribution function of the light in the operation image in the row direction or column direction of the image, the brightness distribution data can be presented in the form of a brightness distribution curve, such as Figure 7 shown.

[0129] In one example, the image analysis device obtains brightness distribution data of at least a portion of the computational image by fitting a function based on information of pixels of the computational image, including the following steps:

[0130] First, location information of a plurality of sub-regions in the at least partial region and a statistical value in each of the sub-regions are obtained.

[0131] Among them, the statistical value of each sub-region includes one of the following values: the average pixel value of the pixels included in each sub-region, the median pixel value of the pixels included in each sub-region, and the pixel value corresponding to the peak value of the pixel histogram of each sub-region.

[0132] At least part of the image (for example, all areas of the entire image) can be divided into multiple sub-areas, such as M×N block areas. The division method of the multiple sub-areas can be reasonably set according to actual needs. For example, the image can be divided into multiple rectangular areas of the same area, such as Figure 4 As shown, it is divided into 10×10 block sub-regions, wherein the position information of each sub-region can be the position information in the image coordinate system, and the origin of the image coordinate system can be any point on the image, for example, Figure 4 As shown in the left figure, a coordinate system can be established with the left vertex of the image as the origin, the width W direction as the X-axis, and the height H direction as the Y-axis, wherein the positive direction of the X-axis is from the left vertex to the right, and the positive direction of the Y-axis is from the left vertex downward. Optionally, the position information of each sub-region can be the position information of the center of each sub-region in the image coordinate system, and its coordinates are marked as (x, y), and the statistical value of each sub-region is z. The statistical value of each region can be calculated by any suitable method known to those skilled in the art (for example, the pixel value can be represented by a brightness value). For example, the average pixel value of each sub-region can be the average value after summing the pixel values ​​of the pixels contained in each sub-region.

[0133] Next, various parameters of the fitting function are solved according to the position information and the average pixel value to determine the brightness distribution function of at least a partial area in the operation image.

[0134] The fitting function is a distribution function of light in the computational image on a two-dimensional plane. For example, the distribution function of light in the two-dimensional plane is Z=f(x, y).

[0135] x, y are the coordinates of each area, and z is the light intensity, which is the pixel value.

[0136] The fitting function may be selected from one of the following functions, such as a polynomial (eg, a quadratic polynomial), an exponential function, a power function, and the like.

[0137] In one example, a quadratic polynomial is selected as the fitting function, and its equation is as follows:

[0138] f(x,y)=p 00 +p 10 ×x+p 01 ×y+p 20 × 2 +p 11 ×x×y+p 02 ×y 2 (1)

[0139] Substitute the position information and statistical values ​​of each area of ​​the operation image, such as the average pixel value, into equation (1) and solve for each parameter p in the equation00 、p 10 、p 01 、p 20 、p 11 、p 02 , that is, the brightness distribution function f(x,y) of the image is obtained.

[0140] It is worth mentioning that in this article, the case where the fitting function is a binary quadratic polynomial is mainly taken as an example, but it can be understood that other types of fitting functions are also applicable to this application.

[0141] Finally, the brightness distribution data of at least a partial area in the operation image is determined according to the brightness distribution function.

[0142] According to the brightness distribution function obtained by the above solution, the brightness distribution surface of the image is determined. According to the range of x in 0-W, W is the width of the image, y is in the range of 0-H, H is the height of the image, and f(x, y) is plotted to obtain the brightness distribution data. The brightness distribution data can be as follows Figure 5 The brightness distribution is presented in the form of a curved surface.

[0143] Among them, x and y of the brightness distribution surface represent the row and column coordinates in the image, and z represents the fitted pixel value of the image.

[0144] Since the brightness distribution surface is obtained through a fitting function based on the information of the pixel points of the computational image, the information of the pixel points of the image includes, for example, the position information and statistical values ​​of multiple areas in the image, the brightness distribution surface retains the information of the image itself, and can reflect the two-dimensional distribution of light on the image. By adjusting the illumination of the image according to the brightness distribution surface, the brightness of the image can be made more uniform while maintaining the information of the image itself.

[0145] In other examples, the fitting function may be a distribution function of brightness in the computed image in the row direction or column direction of the image, that is, based on the information of the pixel points of the computed image, the brightness distribution data of the brightness in the computed image in the row direction or column direction of the image is obtained by the fitting function, which may be calculated by, for example, the following method:

[0146] First, position information of multiple sub-regions in the at least partial region and statistical values ​​within each of the sub-regions are obtained, wherein each of the multiple sub-regions corresponds one-to-one to each column of pixels in the computational image, and the position information includes position information of the column corresponding to each sub-region, such as position information in the image coordinate system, or each of the multiple sub-regions corresponds one-to-one to each row of pixels in the computational image, and the position information includes position information of the row corresponding to each sub-region, such as position information in the image coordinate system.

[0147] like Figure 6 As shown, at least part of the area is the entire area of ​​the image. Taking the distribution in the row direction as an example, the row direction of the image is the positive direction of the x-axis, the column direction of the image is the positive direction of the y-axis, and the image coordinate system is established with the lower left corner of the image as the origin. First, the statistical value of each column of pixels, such as the average pixel value, is calculated. Taking the calculation of the average pixel value as an example, such as the x1 column, the pixel values ​​of all pixels on the image whose x coordinates are equal to x1 are added and averaged to obtain the average pixel value of the pixels in this column. According to the same steps, the average pixel value of each column of pixels is calculated. The value of x ranges from 0-w, where, in the coordinate system with pixels as units, w represents the width of the image in the row direction. For example, w is 2080, and the coordinate of x is any integer between 0 and 2080.

[0148] It is worth mentioning that the solution to the distribution in the column direction can refer to the aforementioned solution method for the distribution in the row direction.

[0149] Next, various parameters of the fitting function are solved according to the position information and the statistical value to determine the brightness distribution function of the operation image.

[0150] The fitting function is a distribution function of the brightness in the operation image in the row direction or column direction of the image. For example, the fitting function includes one of the following functions: a one-dimensional quadratic polynomial, a one-dimensional exponential function, a one-dimensional power function, etc.

[0151] The statistical values ​​of all solved pixels in each column, such as the average pixel value, are fitted to obtain the distribution function f(x).

[0152] Finally, the brightness distribution data of at least a part of the area in the computation image is determined according to the brightness distribution function, that is, the brightness distribution data of the image in the row direction or the column direction, such as Figure 7 For the brightness distribution data of the image in the row direction, the pixels in the same column have the same fitted pixel value, and for the brightness distribution data of the image in the column direction, the pixels in the same row have the same fitted pixel value.

[0153] In this article, since at least part of the area of ​​the calculated image includes pixel points whose actual pixel values ​​are lower than the target value, and / or includes pixel points whose actual pixel values ​​are higher than the target value, since the pixel value is lower than the target value, the field of view at that location is too dark, and it is likely that the true information of the object cannot be seen clearly, while the pixel value of the actual pixel value is higher than the target value, the field of view at that location may be too bright and the true information of the object cannot be seen clearly. Therefore, only these potentially unqualified pixels are subsequently processed to make the brightness of the image more uniform.

[0154] In one example, at least a portion of the area includes all areas of the image, and obtaining the fitted pixel value of each pixel point in at least a portion of the area of ​​the calculated image according to the brightness distribution data includes: obtaining the fitted pixel value of each pixel point in the calculated image according to the brightness distribution data. Figure 5 Find each pixel point on the xy axis of the brightness distribution surface shown in FIG. 1 , and then read the illuminance or brightness value (i.e., the fitted pixel value) corresponding to the pixel point from the z axis of the brightness distribution surface. Alternatively, Figure 6 The x-axis of the brightness distribution data shown is used to find each column of pixels, and then the brightness value (i.e., the fitted pixel value) corresponding to the column of pixels is obtained from the y-axis of the brightness distribution data. The pixels in the same column have the same fitted pixel value, where the brightness value can also reflect the illumination.

[0155] In one example, based on the target value and the eigenvalue, the correction coefficient of the pixel points in at least a partial area of ​​the calculated image is obtained, and it also includes: determining the correction coefficient of each pixel point in the at least partial area according to the target value and the eigenvalue (that is, the fitted pixel value).

[0156] In one example, at least a portion of the area includes all areas of the image, and according to the target value and the fitted pixel value, a correction coefficient for each pixel point in the at least portion of the area is determined, including: according to the target value and the fitted pixel value of each pixel point in the computational image, a correction coefficient matrix is ​​determined, that is, the correction coefficient matrix includes the correction coefficient corresponding to each pixel point, taking the division of the target value and the fitted pixel value as an example, in the example Figure 5 In the brightness distribution data shown, a suitable target value V is set, and V is divided by the fitting pixel value corresponding to each point (i.e., pixel point) on the brightness distribution data to obtain the correction coefficient matrix C(x, y), Alternatively, taking the subtraction of the target value and the fitted pixel value as an example, the correction coefficient matrix C(x, y) = Vf(x, y). Alternatively, taking the division of the target value and the fitted pixel value as an example, in the case Figure 7 In the brightness distribution data shown, a suitable target value V is set, and V is divided by the fitting pixel value corresponding to each point (ie, pixel point) on the brightness distribution data to obtain the correction coefficient matrix g(x).

[0157] The correction coefficient of each pixel can be obtained by any suitable method. For example, the correction coefficient of each pixel point includes a first correction coefficient or a second correction coefficient or a third correction coefficient or a fourth correction coefficient, wherein the first correction coefficient is the ratio of the target value and the characteristic value of the pixel point (for example, the fitted pixel value), the second correction coefficient is the ratio of the characteristic value of the pixel point (for example, the fitted pixel value) to the target value, the third correction coefficient is the difference obtained by subtracting the target value from the characteristic value of the pixel point (for example, the fitted pixel value), and the fourth correction coefficient is the difference obtained by subtracting the characteristic value of the pixel point (for example, the fitted pixel value) from the target value.

[0158] Optionally, the correction coefficient matrix may further include at least one of a first correction coefficient, a second correction coefficient, a third correction coefficient and a fourth correction coefficient.

[0159] It is worth mentioning that, in this article, the target value is pre-set in the image analysis device, or is determined based on the information of the pixel points of the image to be corrected.

[0160] For example, the target value is determined based on the information of the pixel points of the image to be corrected, wherein the target value includes one of the following values: the pixel value of any pixel point in the image to be corrected; the statistical value of the pixel values ​​of at least a part of the area in the image to be corrected, wherein the statistical value is the average pixel value of the pixels included in at least a part of the area, the median of the pixel values ​​of the pixels included in at least a part of the area, or the pixel value corresponding to the peak value of the pixel histogram of at least a part of the area; the fitting pixel value corresponding to any pixel point of the image to be corrected in the brightness distribution data, the calculation method of the brightness distribution data refers to the calculation method of the brightness distribution of the computational image in the previous text, and will not be repeated here. The statistical value of the fitting pixel value corresponding to at least a part of the area of ​​the image to be corrected in the brightness distribution data, the statistical value is the average value of the fitting pixel values ​​of the pixels included in at least a part of the area, the median of the fitting pixel values ​​of the pixels included in at least a part of the area, or the fitting pixel value corresponding to the peak value of the fitting pixel histogram of at least a part of the area.

[0161] For another example, the target value is pre-set in the image analysis device, wherein the target value is a preset empirical value, or a preset value determined based on information of pixel points of an image pre-taken by the imaging device.

[0162] The preset value can be determined based on the information of the pixel points of the image pre-captured by the imaging device by the following method, for example, the pixel value of any pixel point in the image pre-captured by the imaging device; the statistical value of the pixel value of at least a part of the area in the image pre-captured by the imaging device, wherein the statistical value is the average pixel value of the pixel points included in at least a part of the area, the median value of the pixel values ​​of the pixel points included in at least a part of the area, or the pixel value corresponding to the peak value of the pixel histogram of at least a part of the area; the fitting pixel value corresponding to any pixel point in the brightness distribution data of the image pre-captured by the imaging device, the calculation method of the brightness distribution data refers to the calculation method of the brightness distribution of the computational image in the previous text, and will not be repeated here. The statistical value of the fitting pixel value corresponding to at least a part of the area of ​​the image pre-captured by the imaging device in the brightness distribution data, the statistical value is the average value of the fitting pixel values ​​of the pixel points included in at least a part of the area, the median value of the fitting pixel values ​​of the pixel points included in at least a part of the area, or the fitting pixel value corresponding to the peak value of the fitting pixel histogram of at least a part of the area.

[0163] In another embodiment, the image analysis device obtains feature values ​​of at least some of the pixels in at least a portion of the computational image based on the information of the pixels of the computational image, including: first, dividing at least a portion of the computational image into a plurality of sub-regions, for example Figure 8 As shown, all regions of the computational image are divided into a plurality of sub-regions (also referred to as image blocks); then, based on the information of the pixel points of the computational image, a characteristic value (i.e., a statistical value) of each of the sub-regions is obtained, and the characteristic value of each sub-region is used as the characteristic value of the pixel points in the sub-region, wherein the characteristic value of each of the sub-regions includes one of the following statistical values: the average pixel value of the pixel points included in each sub-region, the median value of the pixel values ​​of the pixel points included in each sub-region, the pixel value corresponding to the peak value of the pixel histogram of each sub-region, etc. For example, the calculation is as follows Figure 8 The statistical value of the sub-region shown in the dotted ellipse box, for example, the characteristic value is the average pixel value m, which can be obtained by adding the pixel values ​​of all the pixels included in the sub-region and taking the average, and the statistical value of each sub-region is used as the characteristic value of the pixel points of each sub-region.

[0164] In one example, based on the target value and the characteristic value, the correction coefficient of the pixel points in at least a part of the area of ​​the calculated image is determined, including: based on the target value and the statistical value of each sub-area (that is, the characteristic value of the pixel points in each sub-area), the correction coefficient of the pixel points in each sub-area is determined, for example, if the target value is V, the correction coefficient is obtained by division, and the correction coefficient is equal to the target value V divided by the average pixel value m.

[0165] Optionally, each of the multiple sub-regions corresponds one-to-one to each column of pixels in the computational image, and the characteristic value of each column of pixels, such as the average pixel value, can be calculated, and the correction coefficient of the pixels in each sub-region can be determined by using the target value and the average pixel value, and all the pixels in the column can be corrected. Alternatively, each of the multiple sub-regions corresponds one-to-one to each row of pixels in the computational image, and the characteristic value of each row of pixels can be calculated, such as the average pixel value, and the correction coefficient of the pixels in each row can be determined by using the target value and the average pixel value, and all the pixels in the row can be corrected according to the correction coefficient of the pixels in each row.

[0166] In one example, the correction coefficient of each sub-area may include a first correction coefficient or a second correction coefficient or a third correction coefficient or a fourth correction coefficient, wherein the first correction coefficient is the ratio of the target value and the characteristic value of each pixel point in the sub-area (that is, the characteristic value of each sub-area), the second correction coefficient is the ratio of the characteristic value of each pixel point in the sub-area to the target value, the third correction coefficient is the difference obtained by subtracting the target value from the characteristic value of each pixel point in the sub-area, and the fourth correction coefficient is the difference obtained by subtracting the characteristic value of each pixel point in the sub-area from the target value.

[0167] In another embodiment, the sub-regions may be divided, correction coefficients of some sub-regions may be calculated, and then correction coefficients of all sub-regions may be calculated. For example, the image analysis device obtains characteristic values ​​of at least some pixels of at least some regions in the computational image based on information of pixels of the computational image, including: dividing the at least some regions into a plurality of sub-regions; obtaining statistical values ​​of some sub-regions among the plurality of sub-regions based on information of pixels of the computational image, the statistical values ​​of each sub-region serving as characteristic values ​​of pixels in the sub-region, wherein the statistical values ​​of each of the sub-regions of the partial sub-regions include one of the following values: an average pixel value of pixels included in each sub-region, a median value of pixel values ​​of pixels included in each sub-region, and a pixel value corresponding to a peak value of a pixel histogram of each sub-region.

[0168] The sub-region may be a region including at least two pixels, or may be a region including one pixel. For example, when the sub-region includes only one pixel, the statistical value of the part of the sub-region is also the pixel value of the pixel included in it.

[0169] In a specific example, if Fig. 9 As shown, when a sub-region includes only one pixel point, part of the sub-region is also part of the pixel points. For example, the part of the pixel points are pixel points evenly distributed in at least part of the region, such as Fig. 9The 30*40 points shown in Figure a, in some of the pixel points, adjacent pixel points in the row direction of the image have the same interval and adjacent pixel points in the column direction of the image have the same interval, or some of the pixel points are randomly selected pixel points in at least part of the area.

[0170] Further, based on the target value and the characteristic value, correction coefficients of pixel points in at least a partial area of ​​the computational image are determined, including: determining the correction coefficients of pixel points in each sub-area of ​​the partial sub-areas according to the target value and the characteristic values ​​of pixel points in a partial sub-area among the multiple sub-areas; and solving the correction coefficients corresponding to pixel points in other sub-areas of the at least partial area according to the correction coefficients corresponding to the pixel points in each sub-area of ​​the partial sub-areas, so as to obtain the correction coefficients of all pixel points in the at least partial area.

[0171] In one example, determining the correction coefficient of the pixel point in each of the subregions according to the target value and the characteristic value of the pixel point in the subregions includes:

[0172] The correction coefficients of the pixels in each sub-region of the partial sub-region include a first correction coefficient, a second correction coefficient, a third correction coefficient, or a fourth correction coefficient, wherein the first correction coefficient is the ratio of the target value to the characteristic value of the pixels in each sub-region of the partial sub-region (i.e., the statistical value of the sub-region), the second correction coefficient is the ratio of the characteristic value of the pixels in each sub-region of the partial sub-region to the target value, the third correction coefficient is the difference obtained by subtracting the characteristic value of the pixels in each sub-region of the partial sub-region, and the fourth correction coefficient is the difference obtained by subtracting the characteristic value of the pixels in each sub-region of the partial sub-region from the target value. The correction coefficients of the partial pixels can be determined by division or subtraction, and the appropriate type of correction coefficient can be selected as needed.

[0173] For example, Fig. 9 As shown, according to the target value and the characteristic value, determine Fig. 9 The correction coefficients for 30*40 points are shown in Figure b.

[0174] The correction coefficients corresponding to the pixels in each sub-region of the partial sub-region are solved according to the correction coefficients corresponding to the pixels in other sub-regions of the at least partial region to obtain the correction coefficients of all the pixels in the at least partial region, including: according to the correction coefficients corresponding to the pixels in each sub-region of the partial sub-region, the correction coefficients corresponding to other sub-regions of the at least partial region are interpolated to obtain the correction coefficients of all the pixels in the at least partial region.

[0175] According to Fig. 9 The correction coefficients of the 30*40 points shown in Figure b are determined as follows Fig. 9 The correction coefficients of 1536*2048 points shown in Figure c. Other sub-regions refer to other sub-regions outside the partial sub-regions. The interpolation method can use any suitable method well known to those skilled in the art, such as nearest neighbor interpolation, bilinear interpolation, cubic polynomial interpolation, etc. The correction coefficients of all pixel points may include the correction coefficient corresponding to each pixel point in the partial sub-region, such as the first correction coefficient, the second correction coefficient, the third correction coefficient, or the fourth correction coefficient.

[0176] Based on the above methods, a correction coefficient for correcting the brightness of an image can be obtained.

[0177] In one example, the method of the present invention also includes: updating the correction coefficient at every preset time interval to avoid the situation where the brightness of the image changes due to changes in the lighting conditions of the imaging device over time. For example, at every preset time interval, the correction coefficient is determined based on the pixel point information of the re-acquired computational image, that is, the computational image is re-acquired based on the image taken by the re-acquired imaging device. The re-acquired imaging device can be the latest image taken by the imaging device to better reflect the brightness information of the image when updated.

[0178] Furthermore, at every preset time interval, the brightness distribution of the corrected image is checked, and if the brightness distribution does not meet the preset conditions, the correction coefficient is determined based on the pixel point information of the re-acquired operation image.

[0179] Furthermore, the brightness distribution of the corrected image can be checked at preset time intervals. If the brightness distribution does not meet preset conditions, an alarm is issued and the correction coefficient is updated at the same time.

[0180] The brightness distribution does not meet the preset conditions, for example, when the variance or standard deviation of the brightness distribution of the corrected image exceeds a threshold value, or, it can also be any method known to those skilled in the art for determining whether the image brightness distribution meets the preset conditions, which is not limited here.

[0181] Next, continue to refer to Figure 3In step S302, the image analysis device corrects the brightness of the image to be corrected according to the correction coefficient to obtain an image with uniform brightness. For example, the difference between the pixel value of the pixel point in the corrected image and the target value is made lower than the threshold difference, which can be reasonably set according to prior experience and is not specifically limited here. By correcting the image brightness by the method of the present invention, the clarity and readability of the image output by the image analysis device can be improved, a large number of unqualified images can be avoided, and the user's review efficiency can be improved.

[0182] The image brightness is corrected according to the correction coefficient to obtain a corrected image with uniform brightness, including: the image brightness is corrected according to the actual pixel value of each pixel point in at least a partial area of ​​the calculated image and the correction coefficient corresponding to each pixel point to obtain a corrected image with uniform brightness.

[0183] More specifically, at least part of the area includes all areas of the image. In one example, the image analysis device corrects the brightness of the image to be corrected according to the correction coefficient to obtain an image with uniform brightness, including: correcting the brightness of corresponding pixels in the image to be corrected according to the correction coefficient matrix to obtain a corrected image with uniform brightness, for example, correcting the image brightness according to the actual pixel value of each pixel of the image to be corrected and the correction coefficient corresponding to the pixel to obtain an image with uniform brightness. Since the correction coefficient is obtained based on the information of the pixel points of the calculated image, and the calculated image is obtained from the image taken by the imaging device, adjusting the brightness of the image according to the correction coefficient can make the brightness of the image more uniform while maintaining the image's own information.

[0184] In one example, the image brightness is corrected according to the actual pixel value of each pixel in at least a portion of the image to be corrected and the correction coefficient corresponding to each pixel to obtain an image with uniform brightness, including: multiplying the actual pixel value of each pixel in at least a portion of the calculated image by the first correction coefficient corresponding to each pixel to obtain a corrected pixel value of each pixel, for example, according to Figure 5 The system corrects the input image to be corrected by using the correction coefficient obtained by the fitting method shown in FIG. 1 . Each pixel point in the image to be corrected corresponds to a coordinate (x', y'), and the first correction coefficient at the point is C(x', y'). The actual pixel value of the point is multiplied by the corresponding first correction coefficient to obtain the corrected pixel value. For example, the one-dimensional fitting method is used to obtain the following: Figure 7When the correction coefficient is determined based on the brightness distribution data shown in FIG. 1 , the system corrects the input image to be corrected. Assuming that the actual pixel value of the pixel point (x0, y0) on the image to be corrected is p, the pixel value after correction is the product of p and the first correction coefficient g(x0) corresponding to the pixel point (for example, the correction coefficient of the column where the pixel point is located); for another example, according to Figure 8 When the first correction coefficient obtained by the method shown is used to correct the image to be corrected, each pixel point in each sub-region is multiplied by the first correction coefficient of the sub-region to obtain the corrected pixel value. When each sub-region corresponds to a row of the image to be corrected, each pixel point in each row is multiplied by the first correction coefficient of the row to obtain the corrected pixel value. Or, when each sub-region corresponds to a column of the image, each pixel point in each column is multiplied by the first correction coefficient of the column to obtain the corrected pixel value. For another example, according to Fig. 9 When the first correction coefficient obtained by the interpolation method shown is used to correct the image to be corrected, each pixel in the image to be corrected is multiplied by the first correction coefficient corresponding to each pixel to obtain the corrected pixel value. Finally, the above correction is performed on each pixel in the image to be corrected in turn, and the image to be corrected is updated according to the corrected pixel value to obtain the corrected image.

[0185] In another example, the actual pixel value of each pixel in at least a partial area of ​​the image to be corrected is divided by the second correction coefficient corresponding to each pixel to obtain the corrected pixel value of each pixel; and the image is updated according to the corrected pixel value to obtain an image with uniform brightness.

[0186] In yet another example, the image brightness is corrected according to the actual pixel value of each pixel in at least a partial area of ​​the image to be corrected and the correction coefficient corresponding to each pixel to obtain an image with uniform brightness, including: adding the actual pixel value of each pixel in at least a partial area of ​​the image to be corrected to the third correction coefficient corresponding to each pixel to obtain the corrected pixel value of each pixel; and updating the image to be corrected according to the corrected pixel value to obtain a corrected image.

[0187] In another example, the actual pixel value of each pixel in at least a partial area of ​​the image to be corrected is subtracted from the fourth correction coefficient corresponding to each pixel to obtain the corrected pixel value of each pixel; the image to be corrected is updated according to the corrected pixel value to obtain a corrected image.

[0188] It is worth mentioning that the image to be corrected in this article can be the same image as the operation image used to calculate the correction coefficient in the previous article, or can also be a different image.

[0189] When the lighting factors such as exposure and gain of the machine do not change, a set of correction coefficients can be used to correct all images taken by the image analysis device. For example, the correction coefficients can be calculated offline. The method can refer to the description in the above step S301, and the calculated correction coefficient matrix is ​​stored in the memory of the image analysis device. When the image analysis device needs to correct the captured image, the correction coefficient is directly retrieved from the system to correct the image to be corrected, so as to obtain an image with uniform brightness, and finally the corrected image is output.

[0190] In other examples, a correction coefficient is calculated for each image taken by the image analysis device according to the method in the aforementioned steps, and then each image is corrected according to the correction coefficient obtained.

[0191] In summary, according to the method for correcting image brightness of the present invention, through the above correction method, the pixel value of the pixel point that is too bright is reduced and the pixel value of the pixel point that is too dark is increased, so that the brightness of the corrected image is more uniform, and the display of the information in the image is clearer, which is conducive to user's reference.

[0192] Reference below Fig.10 and Fig.11 The sample analysis system of the present application is described, wherein: Fig.10 A schematic diagram of a sample analysis system in one embodiment of the present invention is shown; Fig.11 A schematic diagram of a control device in one embodiment of the present invention is shown.

[0193] like Fig.10 As shown, the sample analysis system 100 includes a blood analyzer 110, a smear preparation device 120, the aforementioned image analysis device and a control device 140. This article mainly takes the case where the image analysis device is a cell image analysis device 130 as an example, but it can be understood that the image analysis device can also be a case slice analysis device or a urine sediment analysis device, etc.

[0194] The blood analyzer 110 is used to perform routine blood tests on the samples to be tested, the smear preparation device 120 is used to prepare smears of the samples to be tested, the cell image analysis device 130 is used to capture and analyze images of cells in the smear, and the control device 140 is communicatively connected with the blood analyzer 110, the smear preparation device 120 and the cell image analysis device 130.

[0195] The sample analysis system 100 also includes a first transfer track 150 and a second transfer track 160. The first transfer track 150 is used to transport a test tube rack 10a that can hold multiple test tubes 11a loaded with samples to be tested from the blood analyzer 110 to the smear preparation device 120, and the second transfer track 160 is used to transport a glass slide basket 20 that can hold multiple prepared smears 21 from the smear preparation device 120 to the cell image analysis device 130.

[0196] The control device 140 is electrically connected to the first transfer track 150 and the second transfer track 160 and controls the operations thereof.

[0197] The sample analysis system 100 further includes feeding mechanisms 170 and 180 respectively arranged corresponding to the blood analyzer 110 and the smear preparation device 120 , each feeding mechanism 170 and 180 includes a loading buffer area 171 and 181 , a feeding detection area 172 and 182 , and an unloading buffer area 173 and 183 .

[0198] When the test sample on the test tube rack 10a needs to be transported to the blood analyzer 110 for testing, the test tube rack 10a is first transported from the first transfer track 150 to the loading buffer area 171, and then transported from the loading buffer area 171 to the feeding detection area 172 for testing by the blood analyzer 110. After the test is completed, it is unloaded from the feeding detection area 172 to the unloading buffer area 173, and finally enters the first transfer track 150 from the unloading buffer area 173.

[0199] Similarly, when the sample to be tested on the test tube rack 10a needs to be examined under a microscope, the test tube rack 10a needs to be transported to the smear preparation device 120 to prepare a smear. The test tube rack 10a is first transported from the first transmission track 150 to the loading buffer area 181, and then transported from the loading buffer area 181 to the feeding detection area 182 to prepare a smear by the smear preparation device 120. After the smear preparation is completed, it is unloaded from the feeding detection area 182 to the unloading buffer area 183, and finally enters the first transmission track 150 from the unloading buffer area 183. The smear preparation device 120 stores the prepared smear in the glass slide basket 20, and transports the glass slide basket 20 containing the smear to be tested to the cell image analysis device 130 through the second transmission track 160. The cell image analysis device 130 takes images of the cells in the sample on the smear to be tested and analyzes them.

[0200] The sample analysis system 100 further includes a display device (not shown) for displaying sample test results, which may be provided on the blood analyzer 110, the smear preparation device 120, the cell image analysis device 130 or the control device 140, or provided separately.

[0201] like Fig.11The control device 140 includes at least: a processing component 141, a RAM 142, a ROM 143, a communication interface 144, a memory 146 and an I / O interface 145, wherein the processing component 141, the RAM 142, the ROM 143, the communication interface 144, the memory 146 and the I / O interface 145 communicate through a bus 147.

[0202] The processing component 141 may be a CPU, a GPU or other chips with computing capabilities.

[0203] The memory 146 is installed with various computer programs such as operating systems and application programs for the processing component 141 to execute and data required for executing the computer programs. In addition, during the sample detection process, if there is data that needs to be stored locally, it can be stored in the memory 146.

[0204] The I / O interface 145 is composed of serial interfaces such as USB, IEEE1394 or RS-232C, parallel interfaces such as SCSI, IDE or IEEE1284, and analog signal interfaces composed of D / A converters and A / D converters. An input device composed of a keyboard, a mouse, a touch screen or other control buttons is connected to the I / O interface 145, and the user can directly input data to the control device 140 using the input device. In addition, a display with a display function, such as an LCD screen, a touch screen, an LED display screen, etc., can also be connected to the I / O interface 145, and the control device 140 can output the processed data to the display as image display data for display, such as analysis data, instrument operation parameters, etc.

[0205] The communication interface 144 may be an interface of any currently known communication protocol. The communication interface 144 communicates with the outside world through a network. The control device 140 may transmit data with any device connected through the network through the communication interface 144 using a certain communication protocol.

[0206] In one example, a computer program executed by the processing component (e.g., a processor) is stored on the memory, and the processing component is used to execute some steps or all steps of the aforementioned method for correcting image brightness or any combination of the steps. The description of the relevant features in this embodiment can also refer to the method for correcting image brightness in the aforementioned embodiment, and in order to avoid repetition, it will not be described again.

[0207] The sample analysis system of the present invention can correct the brightness to be corrected by obtaining the brightness correction coefficient, so as to make the brightness of the image more uniform, improve the clarity and readability of the image output by the sample analysis system, avoid the appearance of a large number of unqualified images, and improve the user's review efficiency.

[0208] In addition, an embodiment of the present invention further provides a computer storage medium on which a computer program is stored. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor may run the program instructions stored in the storage device to implement the functions (implemented by the processor) in the embodiments of the present invention described herein and / or other desired functions, such as to execute the corresponding steps of the method for correcting image brightness according to an embodiment of the present invention, and various applications and various data may also be stored in the computer-readable storage medium, such as various data used and / or generated by the application.

[0209] For example, the computer storage medium may include a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0210] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely exemplary and are not intended to limit the scope of the present invention thereto. Various changes and modifications may be made therein by one of ordinary skill in the art without departing from the scope and spirit of the present invention. All such changes and modifications are intended to be included within the scope of the present invention as required by the appended claims.

[0211] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0212] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed.

[0213] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0214] Similarly, it should be understood that in order to streamline the present invention and help understand one or more of the various inventive aspects, in the description of the exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, the method of the present invention should not be interpreted as reflecting the following intention: the claimed invention requires more features than the features explicitly stated in each claim. More specifically, as reflected in the corresponding claims, the inventive point is that the corresponding technical problem can be solved with less than all the features of a single disclosed embodiment. Therefore, the claims following the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present invention.

[0215] It will be understood by those skilled in the art that, except for mutually exclusive features, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this specification may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose.

[0216] In addition, those skilled in the art will appreciate that, although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims, any one of the claimed embodiments may be used in any combination.

[0217] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some modules according to embodiments of the present invention. The present invention can also be implemented as a device program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0218] It should be noted that the above embodiments illustrate rather than limit the invention, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of means, several of these means may be embodied by the same hardware item. The use of the words first, second, and third, etc., does not indicate any order. These words may be interpreted as names.

Claims

1. A method for correcting image brightness, characterized in that: The method is applied to an image analysis device; The image analysis device comprises an imaging device; The imaging device includes a lens group and a camera; The lens group includes an objective lens, and the imaging device is used to photograph the observed object in the sample; The method comprises: The image analysis device obtains a correction coefficient of image brightness, including: The image analysis device obtains information of pixel points of the computation image, and based on the information of pixel points of the computation image, obtains feature values ​​of at least some pixel points of at least some area of ​​the computation image; Based on the target value and the characteristic value, a correction coefficient of a pixel point in at least a part of the area of ​​the calculated image is determined, wherein the characteristic value of the pixel point is a brightness characteristic value of the pixel point, which is used to reflect the brightness information of the pixel point; the target value is a preset target brightness information, or a target brightness information determined based on the information of the pixel point of the image to be corrected; The image analysis device corrects the brightness of the image to be corrected according to the correction coefficient to obtain an image with uniform brightness, wherein: The acquiring, based on the information of the pixels of the calculated image, the feature values ​​of at least some pixels of at least some areas of the calculated image comprises: The image analysis device obtains brightness distribution data of at least a part of the operation image through a fitting function based on information of the pixel points of the operation image; obtains a characteristic value of each pixel point of at least a part of the operation image according to the brightness distribution data; or The at least partial area is divided into a plurality of sub-areas; based on the information of the pixel points of the operation image, the statistical values ​​of some sub-areas among the plurality of sub-areas are obtained, and the statistical values ​​of each sub-area in the partial sub-areas are used as the characteristic values ​​of the pixel points respectively included.

2. The method according to claim 1, characterized in that The computational image is a single image taken by the imaging device; or, The computational image is a superimposed image formed by superimposing a plurality of images taken by the imaging device; or, The computational image is a spliced ​​image formed by splicing a plurality of images taken by the imaging device.

3. The method according to claim 1, characterized in that The characteristic value of each pixel point is: the corresponding fitting pixel value of each pixel point in the brightness distribution data.

4. The method according to claim 3, characterized in that The at least partial area includes all areas of the calculated image, and obtaining the characteristic value of each pixel point in at least the partial area of ​​the calculated image according to the brightness distribution data includes: According to the brightness distribution data, a characteristic value of each pixel in the operation image is obtained.

5. The method according to claim 4, characterized in that Determining correction coefficients of pixels of at least a portion of the area based on the target value and the characteristic value includes: A correction coefficient matrix is ​​determined according to the target value and the characteristic value of each pixel in the operation image.

6. The method according to claim 5, characterized in that The image analysis device corrects the brightness of the image to be corrected according to the correction coefficient to obtain an image with uniform brightness, including: The brightness of corresponding pixels in the image to be corrected is corrected according to the correction coefficient matrix to obtain a corrected image with uniform brightness.

7. The method according to claim 3, characterized in that The image analysis device obtains brightness distribution data of at least a part of the computational image by fitting a function based on information of pixels of the computational image, including: Acquire location information of multiple sub-regions in the at least partial region and a statistical value of each of the sub-regions, wherein the statistical value of each of the sub-regions includes one of the following values: an average pixel value of the pixels included in each sub-region, a median value of the pixel values ​​of the pixels included in each sub-region, and a pixel value corresponding to a peak value of a pixel histogram of each sub-region; Solving the parameters of the fitting function according to the position information and the statistical value of each sub-region to determine the brightness distribution function of at least a part of the region in the operation image; The brightness distribution data of at least a partial area in the operation image is determined according to the brightness distribution function.

8. The method according to claim 7, characterized in that The fitting function is a distribution function of brightness in the computational image in a two-dimensional plane; the fitting function includes one of the following functions: a binary quadratic polynomial, an exponential function, and a power function.

9. The method according to claim 1, characterized in that The image analysis device obtains feature values ​​of at least some pixels of at least some areas in the computational image based on information of the pixels of the computational image, including: Dividing at least a portion of the computational image into a plurality of sub-regions; Acquire a characteristic value of each of the sub-regions as a characteristic value of a pixel point of each sub-region, wherein the characteristic value of each of the sub-regions includes one of the following statistical values: an average pixel value of the pixel points included in each sub-region, a median value of the pixel values ​​of the pixel points included in each sub-region, and a pixel value corresponding to a peak value of a pixel histogram of each sub-region.

10. The method according to claim 1, characterized in that The statistical value of each of the sub-regions of the partial sub-region includes one of the following values: the average pixel value of the pixels included in each sub-region, the median value of the pixel values ​​of the pixels included in each sub-region, and the pixel value corresponding to the peak value of the pixel histogram of each sub-region.

11. The method according to claim 10, characterized in that Determining correction coefficients of pixels in at least a portion of the computation image based on the target value and the characteristic value includes: Determine a correction coefficient for a pixel point in each of the subregions according to the target value and a characteristic value of a pixel point in a part of the subregions; According to the correction coefficient corresponding to the pixel point in each sub-region of the partial sub-region, the correction coefficient corresponding to the pixel point in other sub-regions of the at least partial region is solved to obtain the correction coefficient of all the pixel points in the at least partial region.

12. The method according to any one of claims 1 to 11, characterized in that The correction coefficient includes a first correction coefficient, a second correction coefficient, a third correction coefficient, or a fourth correction coefficient, wherein: The first correction coefficient is the ratio of the target value / characteristic value. The second correction coefficient is the ratio of the characteristic value / target value, The third correction coefficient is the difference between the target value and the characteristic value. The fourth correction coefficient is a difference obtained by subtracting the target value from the characteristic value.

13. The method according to claim 12, characterized in that Correcting the brightness of the image to be corrected according to the correction coefficient to obtain an image with uniform brightness, including: The brightness of the image to be corrected is corrected according to the actual pixel value of each pixel point in at least a partial area of ​​the image to be corrected and the correction coefficient corresponding to each pixel point to obtain an image with uniform brightness.

14. The method according to claim 13, characterized in that Correcting the brightness of the image to be corrected according to the actual pixel value of each pixel point in at least a part of the area of ​​the image to be corrected and the correction coefficient corresponding to each pixel point to obtain an image with uniform brightness, including: multiplying the actual pixel value of each pixel point in at least a portion of the image to be corrected by the first correction coefficient corresponding to each pixel point to obtain the corrected pixel value of each pixel point, or dividing the actual pixel value of each pixel point in at least a portion of the image to be corrected by the second correction coefficient corresponding to each pixel point to obtain the corrected pixel value of each pixel point; The image to be corrected is updated according to the corrected pixel values ​​to obtain an image with uniform brightness.

15. The method according to claim 13, characterized in that Correcting the brightness of the image to be corrected according to the actual pixel value of each pixel point in at least a part of the area of ​​the image to be corrected and the correction coefficient corresponding to each pixel point to obtain an image with uniform brightness, including: Adding the actual pixel value of each pixel in at least a partial area of ​​the image to be corrected to the third correction coefficient corresponding to each pixel to obtain the corrected pixel value of each pixel, or subtracting the actual pixel value of each pixel in at least a partial area of ​​the image to be corrected from the fourth correction coefficient corresponding to each pixel to obtain the corrected pixel value of each pixel; The image to be corrected is updated according to the corrected pixel values ​​to obtain an image with uniform brightness.

16. The method according to claim 1, wherein: The target value is determined based on information of the pixel points of the image to be corrected, wherein the target value includes one of the following values: The pixel value of any pixel in the image to be corrected; The statistical value of the pixel values ​​of at least a part of the image to be corrected is a mean pixel value of the pixels included in at least the part of the image, a median value of the pixel values ​​of the pixels included in at least the part of the image, or a pixel value corresponding to a peak value of a pixel histogram of at least the part of the image; The fitted pixel value corresponding to any pixel point of the image to be corrected in the brightness distribution data; The statistical value of the fitted pixel value corresponding to at least a partial area of ​​the image to be corrected in the brightness distribution data is the average value of the fitted pixel values ​​of the pixel points included in at least a partial area, the median value of the fitted pixel values ​​of the pixel points included in at least a partial area, or the fitted pixel value corresponding to the peak value of the fitted pixel histogram of at least a partial area.

17. The method according to claim 1, wherein: The target value is pre-set in the image analysis device, wherein the target value is a preset empirical value, or a preset value determined based on information of pixel points of an image pre-taken by the imaging device.

18. The method of claim 1, wherein: The method further comprises: The correction coefficient is updated at every preset time interval.

19. An image analysis device, characterized in that: The image analysis device comprises: An imaging device, comprising a lens group and a camera, for photographing a sample, wherein the lens group comprises an objective lens; A sample moving device, the sample moving device is used to move the sample relative to the imaging device so that the imaging device captures an image of a specific area in the sample; A processor, configured to execute the method for correcting image brightness as claimed in any one of claims 1 to 18, so as to obtain an image with uniform brightness.

20. The image analysis device according to claim 19, characterized in that: The image analyzing device further comprises a display device, and the display device is used to display the corrected image.

21. A sample analysis system, characterized in that: The sample analysis system comprises: A blood analyzer, used to perform routine blood tests on samples to be tested; A smear preparation device, used to prepare the sample to be tested on a smear; The image analysis device according to claim 19 or 20, used for capturing and analyzing images of the sample to be tested on the smear; A control device is communicatively connected with the blood analyzer, the smear preparation device and the image analysis device.

Images